The field of the invention is systems and methods for detecting seismic wave features in seismic data. More particularly, the invention relates to systems and methods for automatically identifying seismic wave features, such as S-waves, in seismic data.
Seismic events result in the release of surface waves and two types of “body” waves, referred to as “P-waves” and “S-waves.” P-waves are relatively easy to detect in seismic data and this detection can be done automatically by software algorithms. S-waves travel more slowly than P-waves and are much more difficult to detect or “pick.”
Detection of S-waves is desirable for locating the point of origin of earthquakes. S-wave picking can also be useful in microseismic monitoring for assessing the hydraulic fracturing (“fracking”) process. For example, the locations of microseismic events are used to determine fracture network geometry, and their focal mechanisms are helpful for understanding how the fractures are stimulated. The information derived from microseismic monitoring is also helpful for reservoir simulation and assessment. Last but not least, microseismic monitoring in real-time would be useful for assessing induced seismic activity to help avoid inducing large-magnitude earthquakes.
As noted above, correct identification of phase arrival times is very important for seismic event location and identification, source mechanism analysis, and seismic tomography. Phase picking is often done manually, and identification of later arrivals, such as the S-arrival, is complicated by noise from the P-wave coda and because earlier arriving S-to-P converted waves can often be misidentified as the S-arrival.
Today, S-wave picking is often done manually by expert analysts studying seismic data. More recently, attempts have been made at automating S-wave picking, but have yet to achieve reliable accuracy. Examples of S-wave picking methods include those using polarization analysis (Cichowicz, 1993; Reading et al., 2001); neural networks or trees (Wang and Teng, 1997; Gentili and Nichelini, 2006); auto-regressive modeling (Takanami and Kitigama, 1993; Leonard and Kennett, 1999; Kuperkoch et al., 2012); higher-order statistics, especially kurtosis (Savvaidis et al., 2002; Baillard et al., 2014); wavelet analysis (Anant and Dowla, 1997); and combinations of techniques (Patane et al., 2003; Sleeman and van Eck, 2003; Diehl et al., 2009).
Currently available phase detection methods almost exclusively use a parameter-based approach, in which a parameter such as kurtosis or Akaike information criteria (“AIC”) is estimated and relied upon to build a model for determining whether a given window represents a phase arrival. The drawbacks with these approaches include error sources propagating from the parameter estimation to the detection task.
Thus, there remains a need to provide a system and method for identifying seismic features, such as the arrival of S-waves, in seismic data that do not require computing intermediate parameters to perform the detection task.